Update README.md

README.md

@@ -27,24 +27,29 @@ This model is fine-tuned on the SQuAD2.0 dataset. Fine-tuning the biomedical lan
 Huggingface library doesn't implement the Layer-Wise decay feature, which affects the performance on the SQuAD task. The reported result of BioM-ALBERT-xxlarge-SQuAD in our paper is 87.00 (F1) since we use ALBERT open-source code with TF checkpoint, which uses Layer-Wise decay. 
 
 To reproduce results in Google Colab:
+
 - Make sure you have GPU enabled.
+
 - Clone and install required libraries through this code
 
 !git  clone https://github.com/huggingface/transformers
+
 !pip3 install -e transformers
+
 !pip3 install sentencepiece
+
 !pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
 
 - Run this python code:
 
 ```python
-python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \\
---do_eval \\
---version_2_with_negative \\
---per_device_eval_batch_size 8 \\
---dataset_name squad_v2 \\
---overwrite_output_dir \\
---fp16 \\
+python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path BioM-ALBERT-xxlarge-SQuAD2 \
+--do_eval \
+--version_2_with_negative \
+--per_device_eval_batch_size 8 \
+--dataset_name squad_v2 \
+--overwrite_output_dir \
+--fp16 \
 --output_dir out
 ```